Anastasiou, Andreas;
Barp, Alessandro;
Briol, François-Xavier;
Ebner, Bruno;
Gaunt, Robert E;
Ghaderinezhad, Fatemeh;
Gorham, Jackson;
... Swan, Yvik; + view all
(2023)
Stein's method meets computational statistics: A review of some recent developments.
Statistical Science
, 38
(1)
pp. 120-139.
10.1214/22-STS863.
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Abstract
Stein's method compares probability distributions through the study of a class of linear operators called Stein operators. While mainly studied in probability and used to underpin theoretical statistics, Stein's method has led to significant advances in computational statistics in recent years. The goal of this survey is to bring together some of these recent developments and, in doing so, to stimulate further research into the successful field of Stein's method and statistics. The topics we discuss include tools to benchmark and compare sampling methods such as approximate Markov chain Monte Carlo, deterministic alternatives to sampling methods, control variate techniques, parameter estimation and goodness-of-fit testing.
| Type: | Article |
|---|---|
| Title: | Stein's method meets computational statistics: A review of some recent developments |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1214/22-STS863 |
| Publisher version: | https://doi.org/10.1214/22-STS863 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | approximate Markov chain Monte Carlo , control variates , goodness-of-fit testing , likelihood ratio , maximum likelihood estimator , prior sensitivity , sample quality , Stein’s method , variational inference |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10158259 |
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